12,509 research outputs found
GEMINI: A Natural Language System for Spoken-Language Understanding
Gemini is a natural language understanding system developed for spoken
language applications. The paper describes the architecture of Gemini, paying
particular attention to resolving the tension between robustness and
overgeneration. Gemini features a broad-coverage unification-based grammar of
English, fully interleaved syntactic and semantic processing in an all-paths,
bottom-up parser, and an utterance-level parser to find interpretations of
sentences that might not be analyzable as complete sentences. Gemini also
includes novel components for recognizing and correcting grammatical
disfluencies, and for doing parse preferences. This paper presents a
component-by-component view of Gemini, providing detailed relevant measurements
of size, efficiency, and performance.Comment: 8 pages, postscrip
SKOPE: A connectionist/symbolic architecture of spoken Korean processing
Spoken language processing requires speech and natural language integration.
Moreover, spoken Korean calls for unique processing methodology due to its
linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic
spoken Korean processing engine, which emphasizes that: 1) connectionist and
symbolic techniques must be selectively applied according to their relative
strength and weakness, and 2) the linguistic characteristics of Korean must be
fully considered for phoneme recognition, speech and language integration, and
morphological/syntactic processing. The design and implementation of SKOPE
demonstrates how connectionist/symbolic hybrid architectures can be constructed
for spoken agglutinative language processing. Also SKOPE presents many novel
ideas for speech and language processing. The phoneme recognition,
morphological analysis, and syntactic analysis experiments show that SKOPE is a
viable approach for the spoken Korean processing.Comment: 8 pages, latex, use aaai.sty & aaai.bst, bibfile: nlpsp.bib, to be
presented at IJCAI95 workshops on new approaches to learning for natural
language processin
Robust Processing of Natural Language
Previous approaches to robustness in natural language processing usually
treat deviant input by relaxing grammatical constraints whenever a successful
analysis cannot be provided by ``normal'' means. This schema implies, that
error detection always comes prior to error handling, a behaviour which hardly
can compete with its human model, where many erroneous situations are treated
without even noticing them.
The paper analyses the necessary preconditions for achieving a higher degree
of robustness in natural language processing and suggests a quite different
approach based on a procedure for structural disambiguation. It not only offers
the possibility to cope with robustness issues in a more natural way but
eventually might be suited to accommodate quite different aspects of robust
behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro,
pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th
German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture
Notes in Computer Science, Springer 199
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine
translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the
community in this problem has not decreased, and no single method appears to be
strongly dominant across language pairs. Instead, the choice of the optimal
approach for a new translation task still seems to be mostly driven by
empirical trials. To orientate the reader in this vast and complex research
area, we present a comprehensive survey of word reordering viewed as a
statistical modeling challenge and as a natural language phenomenon. The survey
describes in detail how word reordering is modeled within different
string-based and tree-based SMT frameworks and as a stand-alone task, including
systematic overviews of the literature in advanced reordering modeling. We then
question why some approaches are more successful than others in different
language pairs. We argue that, besides measuring the amount of reordering, it
is important to understand which kinds of reordering occur in a given language
pair. To this end, we conduct a qualitative analysis of word reordering
phenomena in a diverse sample of language pairs, based on a large collection of
linguistic knowledge. Empirical results in the SMT literature are shown to
support the hypothesis that a few linguistic facts can be very useful to
anticipate the reordering characteristics of a language pair and to select the
SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic
Robust Grammatical Analysis for Spoken Dialogue Systems
We argue that grammatical analysis is a viable alternative to concept
spotting for processing spoken input in a practical spoken dialogue system. We
discuss the structure of the grammar, and a model for robust parsing which
combines linguistic sources of information and statistical sources of
information. We discuss test results suggesting that grammatical processing
allows fast and accurate processing of spoken input.Comment: Accepted for JNL
SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks
In this paper, we describe a so-called screening approach for learning robust
processing of spontaneously spoken language. A screening approach is a flat
analysis which uses shallow sequences of category representations for analyzing
an utterance at various syntactic, semantic and dialog levels. Rather than
using a deeply structured symbolic analysis, we use a flat connectionist
analysis. This screening approach aims at supporting speech and language
processing by using (1) data-driven learning and (2) robustness of
connectionist networks. In order to test this approach, we have developed the
SCREEN system which is based on this new robust, learned and flat analysis.
In this paper, we focus on a detailed description of SCREEN's architecture,
the flat syntactic and semantic analysis, the interaction with a speech
recognizer, and a detailed evaluation analysis of the robustness under the
influence of noisy or incomplete input. The main result of this paper is that
flat representations allow more robust processing of spontaneous spoken
language than deeply structured representations. In particular, we show how the
fault-tolerance and learning capability of connectionist networks can support a
flat analysis for providing more robust spoken-language processing within an
overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial
Intelligence Research 6(1), 199
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